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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

4

보존된 평가 기록

5

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Umang-pandey/vae-gan-tf

Autoencoding beyond pixels using a learned similarity metric

https://github.com/Unbabel/OpenNMT-py

OpenNMT: Neural Machine Translation Toolkit

https://github.com/Unbabel/sparse_constrained_attention

Sparse and Constrained Attention for Neural Machine Translation

https://github.com/VKCOM/YouTokenToMe

Neural Machine Translation of Rare Words with Subword Units

https://github.com/Varchita-Beena/Word-Translation-Without-Parallel-Data

Word Translation Without Parallel Data

https://github.com/VedantDere0104/CoMo_GAN

CoMoGAN: continuous model-guided image-to-image translation

https://github.com/VedantDere0104/Cycle_GAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/VedantDere0104/Pix2Pix_GAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/VedantYadav/Transformer

Attention Is All You Need

https://github.com/VhalPurohit/290s

Stochastic Gradient Push for Distributed Deep Learning

https://github.com/Vicent-xd/residual_encoding_net

Autoencoding beyond pixels using a learned similarity metric

https://github.com/VigneshSrinivasan10/langevin_cooling

Langevin Cooling for Domain Translation

https://github.com/Vikas-Sony/machine-translation

OpenNMT: Open-Source Toolkit for Neural Machine Translation

https://github.com/Vikas-Sony/speech-to-text

Neural Machine Translation in Linear Time

https://github.com/VinitSR7/Image-Caption-Generation

Show and Tell: A Neural Image Caption Generator

https://github.com/Vious/LBAM_Pytorch

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/ViswanathaReddyGajjala/EfficientNet-RetinaNet

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/VladAndronik/shadow-gans

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/VladimirV99/Automatic-Translation

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

https://github.com/Vladislav-IS/dls_project_pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/VyshnaviSK/Face2Mask-CycleGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/W4ngatang/qags

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/W95Psp/NID-results

Normalized Information Distance

https://github.com/Waino/hnmt

Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character Models

https://github.com/Walter-B/bert-20-classes

Attention Is All You Need

https://github.com/WeiYangze/hibernate-demo

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/WenYanger/General-Transformer-Pytorch

Attention Is All You Need

https://github.com/Wence-May/Image-Annotation

Show and Tell: A Neural Image Caption Generator

https://github.com/Wenchao-Du/LIR-for-Unsupervised-IR

Unsupervised Image-to-Image Translation Networks

https://github.com/WenjieDu/PyPOTS

Attention Is All You Need

https://github.com/WhiskyChoy/language-style-transfer

Style Transfer from Non-Parallel Text by Cross-Alignment

https://github.com/William-N-Havard/VGS-dataset-metadata

STAIR Captions: Constructing a Large-Scale Japanese Image Caption Dataset

https://github.com/Wizaron/coord-conv-pytorch

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/Wolfie8935/Implementation-of-Attention-is-all-you-need

Attention Is All You Need

https://github.com/WuZhewei/my_First_GAN_tryout

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/XINZXZHANG/Bert_repo

Attention Is All You Need

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.